Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.

模拟 COVID-19 等原发性急性病毒感染的组织特异性效应对于了解疾病结果和优化治疗至关重要。此类模拟需要支持持续更新，以响应对感染机制理解的快速进展，以及多个小组并行开发组件。我们提出了一个用于上皮组织、病毒感染、细胞免疫反应和组织损伤的多尺度时空模拟的开源平台，专门设计为模块化和可扩展的，以支持持续更新和并行开发。上皮组织和免疫反应的简化斑块的基础模拟表现出从广泛感染到复发再到清除的不同感染动态模式。较慢的病毒内化和较快的免疫细胞招募可减缓感染并促进遏制。由于抗病毒药物可能会产生副作用，并且在感染期间稍后给予时会降低临床疗效，因此我们研究了其对治疗效力进展和感染后首次治疗时间的影响。在模拟中，即使是使用降低病毒 RNA 复制率的低效药物治疗，当在感染开始时进行治疗时，也能大大减少总组织损伤和病毒负荷。剂量和治疗时间的许多组合会导致随机结果，一些模拟复制品显示清除或控制（治疗成功），而另一些则显示所有上皮细胞的快速感染（治疗失败）。因此，虽然较晚给予的高效治疗通常效果较差，但较晚的治疗有时会有效。我们说明了如何使用我们的软件模块和公开可用的软件存储库扩展该平台以模拟特定病毒类型（例如丙型肝炎）并添加额外的细胞机制（组织恢复和可变细胞对感染的易感性） 。